Dragan Petrovic

Overcoming untuned radios in wireless networks with network coding

The drive toward the implementation and massive deployment of wireless sensor networks calls for ultralow-cost and low-power nodes. While the digital subsystems of the nodes are still following Moores Law, there is no such trend regarding the performance of analog components. This work proposes a fully integrated architecture of both digital and analog components (including local oscillator) that offers significant reduction in cost, size, and overall power consumption of the node. Even though such a radical architecture cannot offer the reliable tuning of standard designs, it is shown that by using random network coding, a dense network of such nodes can achieve throughput linear in the number of channels available for communication. Moreover, the ratio of the achievable throughput of the untuned network to the throughput of a tuned network with perfect coordination is shown to be close to 1/e. This work uses network coding to leverage the fact that throughput equal to the max-flow in a graph is achievable even if the topology is not know a priori. However, the challenge here is finding the max-flow of the random graph corresponding to the network.

Information-directed routing in ad hoc sensor networks

In a sensor network, data routing is tightly coupled to the needs of a sensing task, and hence the application semantics. This paper introduces the novel idea of information-directed routing, in which routing is formulated as a joint optimization of data transport and information aggregation. The routing objective is to minimize communication cost, while maximizing information gain, differing from routing considerations for more general ad hoc networks. The paper uses the concrete problem of locating and tracking possibly moving signal sources as an example of information generation process, and considers two common information extraction patterns in a sensor network: routing a user query from an arbitrary entry node to the vicinity of signal sources and back, or to a prespecified exit node, maximizing information accumulated along the path. We derive information constraints from realistic signal models, and present several routing algorithms that find near-optimal solutions for the joint optimization problem. Simulation results have demonstrated that information-directed routing is a significant improvement over a previously reported greedy algorithm, as measured by sensing quality such as localization and tracking accuracy and communication quality such as success rate in routing around sensor holes.

Tracking and exploiting correlations in dense sensor networks

In this paper, we propose a novel method for reducing energy consumption in a sensor network. It is important in a sensor network to minimize the energy usage of each sensor, because the nodes typically have finite battery life and if a node dies, this can lead to a loss of data or a network partition. As a result, several researchers have proposed various methods of routing and communication between nodes to reduce energy consumption. We propose an orthogonal approach to previous methods. In particular, we propose to exploit the inherent correlations that exist between sensor nodes by devising a novel algorithm that enables sensor nodes to compress their readings without knowing the exact measurements of the other nodes. Our simulations show that our algorithm used is promising as it leads to significant energy saving for various types of sensor nodes.

A distributed and adaptive signal processing approach to exploiting correlation in sensor networks

Abstract We propose a novel approach to reducing energy consumption in sensor networks using a distributed adaptive signal processing framework and efficient algorithm. 1 While the topic of energy-aware routing to alleviate energy consumption in sensor networks has received attention recently [C. Toh, IEEE Commun. Mag. June (2001) 138; R. Shah, J. Rabaey, Proc. IEEE WCNC, March 2002], in this paper, we propose an orthogonal approach to complement previous methods. Specifically, we propose a distributed way of continuously exploiting existing correlations in sensor data based on adaptive signal processing and distributed source coding principles. Our approach enables sensor nodes to blindly compress their readings with respect to one another without the need for explicit and energy-expensive inter-sensor communication to effect this compression. Furthermore, the distributed algorithm used by each sensor node is extremely low in complexity and easy to implement (i.e., one modulo operation), while an adaptive filtering framework is used at the data gathering unit to continuously learn the relevant correlation structures in the sensor data. Applying the algorithm to testbed data resulted in energy savings of 10–65% for a multitude of sensor modalities.

Coding for sensor networks using untuned radios

The drive toward the implementation and massive deployment of wireless sensor networks calls for ultra-low-cost and low-power nodes. While the digital subsystems of the nodes are still riding Moores Law, there is no such trend regarding the performance of analog components. This work presents a fully integrated architecture of both digital and analog components (including local oscillator) that offers significant reduction in cost, size and power consumption of the overall node. While such a radical architecture cannot offer the reliable tuning of standard designs, it is shown that by using randomized signal processing techniques, a dense network of such nodes can achieve throughput linear in the number of channels available for communication. Moreover, the ratio of the achievable throughput of the untuned network to the throughput of a tuned network with perfect coordination is shown to be close to 1/e.

List Viterbi decoding with continuous error detection for magnetic recording

The list Viterbi algorithm (LVA) with an arithmetic coding based continuous error detection (CED) scheme is applied to high-order partial-response magnetic recording channels. Commonly used magnetic recording systems employ distance-enhancing codes along with parity-check post-processors to correct most dominant error events. The system presented here inserts a CED encoder between the outer Reed-Solomon (RS) code and the channel, and utilizes LVA along with CEDs error detection capabilities to improve the performance of the Viterbi decoder. Simulations show that this system results in a 2 dB improvement over MTR encoded EEPRML decoding at a BER of 2/spl times/10/sup -6/ in additive white Gaussian noise and localizes error occurrences to the end of the sector.